Surveillance systems, more specifically video surveillance systems, have been widely used in a variety of industries. Recent demands on accuracy and timeliness exposes several problems of existing surveillance systems. For example, existing surveillance systems only passively record a situation, but do not perform any analysis, let alone making a proper decision to respond to the situation. They heavily reply on security officers to analyze the situation and handle it properly. Some surveillance systems may have an ability to analyze a video of a situation based on computer vision techniques. However, the decision making process is still left to the security officers. If the security officers are not informed in time, for example, if the security offers are temporarily absent, away from the monitors of the systems, or just taking eyes off the monitor for a few minutes due to tiredness, then the whole surveillance systems are meaningless. Many urgent situations will be missed.
Further, existing surveillance systems are not flexible. Once installed, they cannot move. Some surveillance systems may have pan-tilt-zoom functions, but the positions of cameras are fixed. Even carefully designed, such systems may still have dead angles. This problem is getting worse where surrounding environment changes, while the surveillance systems cannot change accordingly. In addition to the problem of dead angle or the like, fixed cameras may have accuracy problem. For example, in face recognition, the algorithm usually has a maximum size and a minimum size limitation on a person in an image. However, when the person is approaching from a distance, which occurs frequently in real life, the size difference of the person in the image may be significant, exceeding the limitations of the face recognition algorithm and thus decreasing the accuracy in face recognition.